There are many situations where user input into a computing system is useful to gather large amounts of information about a user. Accurate information about users is useful for training and safety in many industries. Gathering information should be discreet, unobtrusive, and should not negatively impact performance. In some contexts, similar information patterns might need a different interpretation. In other situations, the user may be limited in ability to input information into a computing system or to use and control an electronic device. It would be a further advantage if such input systems could be ubiquitous so that they become second nature and automatic as the brain of the user adapts to the input and control mechanisms.
A brain computer interface would be the ideal, but would require inconvenient or cumbersome wearable devices and is not precise enough. Biometric identification is used for many applications including unlocking mobile phones as well as access to digital and physical spaces. However, biometric identification is limited in what it can do. For example, biometric identification can only identify the user, not the intentions of the user or help the user to achieve a goal other than entry.
“Smart textiles” have been developed that are laden with sensors to capture data automatically from the user continuously and without the user thinking about it. However, smart textiles have the limitations of needing to be changed and washed, to be powered or laden with electronics or sensors, and to match the fashion sense of the user.
Wearable devices have been described including wrist-worn and finger-worn devices and have been successful in capturing user activity and other sensor data. However, wearable devices have not succeeded as input devices because they also do not adapt to the intentions and goals of the user in order to seamlessly accomplish a range of desired tasks as a training device. Conventional gesture detection systems are either a tactile input (like touch screens), detected with cameras, or are detected through a combination of beam and motion detection on a larger device.
When performing a physical activity such as sports and other types of performances it is not practical for a user to input data into a computing system. Physical activity involves accurate movement, lots of training, and often has restrictions on the type of clothing/equipment that can be worn. A user performing a physical activity cannot stop to input user data and cannot be encumbered by obtrusive input systems. Subtle differences in motion are sometimes the difference between a successful execution of an action and an unsuccessful execution of an action.
Concussions and other head injuries are a big issue in sports, especially for children. Head injuries are also an issue for members of the military. One of the most common injuries faced by military veterans is traumatic brain injury from IEDs. The importance of monitoring head injuries has gained awareness due to high profile incidents. Sports organizations are increasingly focusing on ensuring player safety when potential head injuries are suspected.
Many players want to stay in the game, even when injured. Coaches, fans, and team owners do not want star players sitting on the bench. Players sometimes seem healthy but symptoms of injury are not noticeable until later. Players that have sustained a head injury are at a higher risk of sustaining an even more severe injury. However, head injuries are highly individualized events and richer data sets are needed to determine whether a player has sustained an injury and/or is at risk of sustaining further injury.
In amateur sports there is an increasing need for solutions that indicate to coaches and physical therapists when an athlete has suffered a risky head blow. Often the athlete appears unaffected by a head collision, continues to play, only to collapse later suffering further injury.
It would be desirable to implement a wearable user input device and sensor system capable of detecting injury.